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Abstract Proton exchange membrane (PEM) fuel cell faces the inevitable challenge of the cold start at a sub‐freezing temperature. Understanding the underlying degradation mechanisms in the cold start and developing a better starting strategy to achieve a quick startup with no degradation are essential for the wide application of PEM fuel cells. In this study, the comprehensive in situ non‐accelerated segmented techniques are developed to analyze the icing processes and obtain the degradation mechanisms under the conditions of freeze–thaw cycle, voltage reversal, and ice formation in different components of PEM fuel cells for different freezing time. A detailed degradation mechanism map in the cold start of PEM fuel cells is proposed to demonstrate how much degradation occurs under different conditions, whether the ice formation is acceptable under the actual operating conditions, and how to suppress the ice formation. Moreover, an ideal starting strategy is developed to achieve the cold start of PEM fuel cells without degradation. This map is highly valuable and useful for researchers to understand the underlying degradation mechanisms and develop the cold start strategy, thereby promoting the commercialization of PEM fuel cells.

The compression of the gas diffusion layer (GDL) greatly affects the electrochemical performance of proton exchange membrane fuel cells (PEMFCs) by means of both the equivalent value and distribution of contact pressure, which depends on the packing manner of the fuel cell. This work develops an intelligent approach for improving the uniformity and equivalent magnitude of contact pressure on GDLs through optimizing the clamping forces and positions on end plates. A finite element (FE) model of a full-size single fuel cell is developed and correlated against a direct measurement of pressure between the GDL and a bipolar plate. Datasets generated by FE simulations based on the optimal Latin hypercube design are used as a driving force for the training of a radial basis function neural network, so-called the agent model. Once the agent model is validated, iterations for optimization of contact pressure on GDLs are carried out without using the complicated physical model anymore. Optimal design of clamping force and position combination is achieved in terms of better contact pressure, with the designed equivalent magnitude and more uniform distribution. Results indicate the proposed agent-based intelligent optimization approach is available for the packing design of fuel cells, stacks in particular, with significantly higher efficiency.